DonorsChoose¶
DonorsChoose.org receives hundreds of thousands of project proposals each year for classroom projects in need of funding. Right now, a large number of volunteers is needed to manually screen each submission before it's approved to be posted on the DonorsChoose.org website.
Next year, DonorsChoose.org expects to receive close to 500,000 project proposals. As a result, there are three main problems they need to solve:
- How to scale current manual processes and resources to screen 500,000 projects so that they can be posted as quickly and as efficiently as possible
- How to increase the consistency of project vetting across different volunteers to improve the experience for teachers
- How to focus volunteer time on the applications that need the most assistance
The goal of the competition is to predict whether or not a DonorsChoose.org project proposal submitted by a teacher will be approved, using the text of project descriptions as well as additional metadata about the project, teacher, and school. DonorsChoose.org can then use this information to identify projects most likely to need further review before approval.
About the DonorsChoose Data Set¶
The train.csv data set provided by DonorsChoose contains the following features:
| Feature | Description |
|---|---|
project_id |
A unique identifier for the proposed project. Example: p036502 |
project_title |
Title of the project. Examples:
|
project_grade_category |
Grade level of students for which the project is targeted. One of the following enumerated values:
|
project_subject_categories |
One or more (comma-separated) subject categories for the project from the following enumerated list of values:
Examples:
|
school_state |
State where school is located (Two-letter U.S. postal code). Example: WY |
project_subject_subcategories |
One or more (comma-separated) subject subcategories for the project. Examples:
|
project_resource_summary |
An explanation of the resources needed for the project. Example:
|
project_essay_1 |
First application essay* |
project_essay_2 |
Second application essay* |
project_essay_3 |
Third application essay* |
project_essay_4 |
Fourth application essay* |
project_submitted_datetime |
Datetime when project application was submitted. Example: 2016-04-28 12:43:56.245 |
teacher_id |
A unique identifier for the teacher of the proposed project. Example: bdf8baa8fedef6bfeec7ae4ff1c15c56 |
teacher_prefix |
Teacher's title. One of the following enumerated values:
|
teacher_number_of_previously_posted_projects |
Number of project applications previously submitted by the same teacher. Example: 2 |
* See the section Notes on the Essay Data for more details about these features.
Additionally, the resources.csv data set provides more data about the resources required for each project. Each line in this file represents a resource required by a project:
| Feature | Description |
|---|---|
id |
A project_id value from the train.csv file. Example: p036502 |
description |
Desciption of the resource. Example: Tenor Saxophone Reeds, Box of 25 |
quantity |
Quantity of the resource required. Example: 3 |
price |
Price of the resource required. Example: 9.95 |
Note: Many projects require multiple resources. The id value corresponds to a project_id in train.csv, so you use it as a key to retrieve all resources needed for a project:
The data set contains the following label (the value you will attempt to predict):
| Label | Description |
|---|---|
project_is_approved |
A binary flag indicating whether DonorsChoose approved the project. A value of 0 indicates the project was not approved, and a value of 1 indicates the project was approved. |
Notes on the Essay Data¶
-
Prior to May 17, 2016, the prompts for the essays were as follows:
- __project_essay_1:__ "Introduce us to your classroom"
- __project_essay_2:__ "Tell us more about your students"
- __project_essay_3:__ "Describe how your students will use the materials you're requesting"
- __project_essay_3:__ "Close by sharing why your project will make a difference"
-
Starting on May 17, 2016, the number of essays was reduced from 4 to 2, and the prompts for the first 2 essays were changed to the following:
- __project_essay_1:__ "Describe your students: What makes your students special? Specific details about their background, your neighborhood, and your school are all helpful."
- __project_essay_2:__ "About your project: How will these materials make a difference in your students' learning and improve their school lives?"
For all projects with project_submitted_datetime of 2016-05-17 and later, the values of project_essay_3 and project_essay_4 will be NaN.
In [2]:
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
import sqlite3
import pandas as pd
import numpy as np
import nltk
import string
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import confusion_matrix
from sklearn import metrics
from sklearn.metrics import roc_curve, auc
from nltk.stem.porter import PorterStemmer
import re
# Tutorial about Python regular expressions: https://pymotw.com/2/re/
import string
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem.wordnet import WordNetLemmatizer
from gensim.models import Word2Vec
from gensim.models import KeyedVectors
import pickle
from tqdm import tqdm
import os
from plotly import plotly
import plotly.offline as offline
import plotly.graph_objs as go
offline.init_notebook_mode()
from collections import Counter
1.1 Reading Data¶
In [3]:
project_data = pd.read_csv('train_data.csv')
resource_data = pd.read_csv('resources.csv')
In [4]:
print("Number of data points in train data", project_data.shape)
print('-'*50)
print("The attributes of data :", project_data.columns.values)
In [5]:
print("Number of data points in train data", resource_data.shape)
print(resource_data.columns.values)
resource_data.head(2)
Out[5]:
In [6]:
# https://stackoverflow.com/questions/22407798/how-to-reset-a-dataframes-indexes-for-all-groups-in-one-step
price_data = resource_data.groupby('id').agg({'price':'sum', 'quantity':'sum'}).reset_index()
# join two dataframes in python:
project_data = pd.merge(project_data, price_data, on='id', how='left')
In [7]:
# #Sampling down the data
project_data = project_data.sample(frac=0.5)
1.2 preprocessing of project_subject_categories¶
In [8]:
catogories = list(project_data['project_subject_categories'].values)
# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039
# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/
# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string
# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python
cat_list = []
for i in catogories:
temp = ""
# consider we have text like this "Math & Science, Warmth, Care & Hunger"
for j in i.split(','): # it will split it in three parts ["Math & Science", "Warmth", "Care & Hunger"]
if 'The' in j.split(): # this will split each of the catogory based on space "Math & Science"=> "Math","&", "Science"
j=j.replace('The','') # if we have the words "The" we are going to replace it with ''(i.e removing 'The')
j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:"Math & Science"=>"Math&Science"
temp+=j.strip()+" " #" abc ".strip() will return "abc", remove the trailing spaces
temp = temp.replace('&','_') # we are replacing the & value into
cat_list.append(temp.strip())
project_data['clean_categories'] = cat_list
project_data.drop(['project_subject_categories'], axis=1, inplace=True)
from collections import Counter
my_counter = Counter()
for word in project_data['clean_categories'].values:
my_counter.update(word.split())
cat_dict = dict(my_counter)
sorted_cat_dict = dict(sorted(cat_dict.items(), key=lambda kv: kv[1]))
1.3 preprocessing of project_subject_subcategories¶
In [9]:
sub_catogories = list(project_data['project_subject_subcategories'].values)
# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039
# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/
# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string
# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python
sub_cat_list = []
for i in sub_catogories:
temp = ""
# consider we have text like this "Math & Science, Warmth, Care & Hunger"
for j in i.split(','): # it will split it in three parts ["Math & Science", "Warmth", "Care & Hunger"]
if 'The' in j.split(): # this will split each of the catogory based on space "Math & Science"=> "Math","&", "Science"
j=j.replace('The','') # if we have the words "The" we are going to replace it with ''(i.e removing 'The')
j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:"Math & Science"=>"Math&Science"
temp +=j.strip()+" "#" abc ".strip() will return "abc", remove the trailing spaces
temp = temp.replace('&','_')
sub_cat_list.append(temp.strip())
project_data['clean_subcategories'] = sub_cat_list
project_data.drop(['project_subject_subcategories'], axis=1, inplace=True)
# count of all the words in corpus python: https://stackoverflow.com/a/22898595/4084039
my_counter = Counter()
for word in project_data['clean_subcategories'].values:
my_counter.update(word.split())
sub_cat_dict = dict(my_counter)
sorted_sub_cat_dict = dict(sorted(sub_cat_dict.items(), key=lambda kv: kv[1]))
1.3 Text preprocessing¶
In [10]:
# merge two column text dataframe:
project_data["essay"] = project_data["project_essay_1"].map(str) +\
project_data["project_essay_2"].map(str) + \
project_data["project_essay_3"].map(str) + \
project_data["project_essay_4"].map(str)
In [11]:
project_data.head(2)
Out[11]:
In [12]:
#### 1.4.2.3 Using Pretrained Models: TFIDF weighted W2V
In [13]:
# printing some random reviews
print(project_data['essay'].values[0])
print("="*50)
print(project_data['essay'].values[150])
print("="*50)
print(project_data['essay'].values[1000])
print("="*50)
print(project_data['essay'].values[20000])
print("="*50)
# print(project_data['essay'].values[99999])
# print("="*50)
In [14]:
# https://stackoverflow.com/a/47091490/4084039
import re
def decontracted(phrase):
# specific
phrase = re.sub(r"won't", "will not", phrase)
phrase = re.sub(r"can\'t", "can not", phrase)
# general
phrase = re.sub(r"n\'t", " not", phrase)
phrase = re.sub(r"\'re", " are", phrase)
phrase = re.sub(r"\'s", " is", phrase)
phrase = re.sub(r"\'d", " would", phrase)
phrase = re.sub(r"\'ll", " will", phrase)
phrase = re.sub(r"\'t", " not", phrase)
phrase = re.sub(r"\'ve", " have", phrase)
phrase = re.sub(r"\'m", " am", phrase)
return phrase
In [15]:
sent = decontracted(project_data['essay'].values[20000])
print(sent)
print("="*50)
In [16]:
# \r \n \t remove from string python: http://texthandler.com/info/remove-line-breaks-python/
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
print(sent)
In [17]:
#remove spacial character: https://stackoverflow.com/a/5843547/4084039
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
print(sent)
In [18]:
# https://gist.github.com/sebleier/554280
# we are removing the words from the stop words list: 'no', 'nor', 'not'
stopwords= ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've",\
"you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', \
'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their',\
'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', \
'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', \
'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', \
'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after',\
'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further',\
'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more',\
'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so', 'than', 'too', 'very', \
's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', \
've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn',\
"hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn',\
"mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", \
'won', "won't", 'wouldn', "wouldn't"]
In [19]:
# Combining all the above stundents
from tqdm import tqdm
preprocessed_essays = []
# tqdm is for printing the status bar
for sentance in tqdm(project_data['essay'].values):
sent = decontracted(sentance)
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
# https://gist.github.com/sebleier/554280
sent = ' '.join(e for e in sent.split() if e not in stopwords)
preprocessed_essays.append(sent.lower().strip())
In [20]:
# after preprocesing
preprocessed_essays[20000]
Out[20]:
1.4 Preprocessing of `project_title`
In [21]:
# similarly you can preprocess the titles also
# similarly you can preprocess the titles also
from tqdm import tqdm
preprocessed_titles = []
# tqdm is for printing the status bar
for sentance in tqdm(project_data['project_title'].values):
sent = decontracted(sentance)
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
# https://gist.github.com/sebleier/554280
sent = ' '.join(e for e in sent.split() if e.lower() not in stopwords)
preprocessed_titles.append(sent.lower().strip())
preprocessed_titles[1]
Out[21]:
In [22]:
preprocessed_essays[20000]
project_data['clean_essays'] = preprocessed_essays
project_data.drop(['project_essay_1'], axis=1, inplace=True)
project_data.drop(['project_essay_2'], axis=1, inplace=True)
project_data.drop(['project_essay_3'], axis=1, inplace=True)
project_data.drop(['project_essay_4'], axis=1, inplace=True)
In [23]:
project_data['clean_titles'] = preprocessed_titles
1.5 Preparing data for models¶
In [24]:
project_data.columns
Out[24]:
we are going to consider
- school_state : categorical data
- clean_categories : categorical data
- clean_subcategories : categorical data
- project_grade_category : categorical data
- teacher_prefix : categorical data
- project_title : text data
- text : text data
- project_resource_summary: text data (optinal)
- quantity : numerical (optinal)
- teacher_number_of_previously_posted_projects : numerical
- price : numerical1.5.1 Vectorizing Categorical data¶
In [25]:
# # we use count vectorizer to convert the values into one
# from sklearn.feature_extraction.text import CountVectorizer
# vectorizer = CountVectorizer(vocabulary=list(sorted_cat_dict.keys()), lowercase=False, binary=True)
# categories_one_hot = vectorizer.fit_transform(project_data['clean_categories'].values)
# print(vectorizer.get_feature_names())
# print("Shape of matrix after one hot encodig ",categories_one_hot.shape)
In [26]:
# # we use count vectorizer to convert the values into one
# vectorizer = CountVectorizer(vocabulary=list(sorted_sub_cat_dict.keys()), lowercase=False, binary=True)
# sub_categories_one_hot = vectorizer.fit_transform(project_data['clean_subcategories'].values)
# print(vectorizer.get_feature_names())
# print("Shape of matrix after one hot encodig ",sub_categories_one_hot.shape)
In [27]:
# you can do the similar thing with state, teacher_prefix and project_grade_category also
1.5.2 Vectorizing Text data¶
1.5.2.1 Bag of words¶
In [28]:
# # We are considering only the words which appeared in at least 10 documents(rows or projects).
# vectorizer = CountVectorizer(min_df=10)
# text_bow = vectorizer.fit_transform(preprocessed_essays)
# print("Shape of matrix after one hot encodig ",text_bow.shape)
In [29]:
# you can vectorize the title also
# before you vectorize the title make sure you preprocess it
1.5.2.2 TFIDF vectorizer¶
In [30]:
# from sklearn.feature_extraction.text import TfidfVectorizer
# vectorizer = TfidfVectorizer(min_df=10)
# text_tfidf = vectorizer.fit_transform(preprocessed_essays)
# print("Shape of matrix after one hot encodig ",text_tfidf.shape)
1.5.2.3 Using Pretrained Models: Avg W2V¶
In [31]:
'''
# Reading glove vectors in python: https://stackoverflow.com/a/38230349/4084039
def loadGloveModel(gloveFile):
print ("Loading Glove Model")
f = open(gloveFile,'r', encoding="utf8")
model = {}
for line in tqdm(f):
splitLine = line.split()
word = splitLine[0]
embedding = np.array([float(val) for val in splitLine[1:]])
model[word] = embedding
print ("Done.",len(model)," words loaded!")
return model
model = loadGloveModel('glove.42B.300d.txt')
# ============================
Output:
Loading Glove Model
1917495it [06:32, 4879.69it/s]
Done. 1917495 words loaded!
# ============================
words = []
for i in preproced_texts:
words.extend(i.split(' '))
for i in preproced_titles:
words.extend(i.split(' '))
print("all the words in the coupus", len(words))
words = set(words)
print("the unique words in the coupus", len(words))
inter_words = set(model.keys()).intersection(words)
print("The number of words that are present in both glove vectors and our coupus", \
len(inter_words),"(",np.round(len(inter_words)/len(words)*100,3),"%)")
words_courpus = {}
words_glove = set(model.keys())
for i in words:
if i in words_glove:
words_courpus[i] = model[i]
print("word 2 vec length", len(words_courpus))
# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/
import pickle
with open('glove_vectors', 'wb') as f:
pickle.dump(words_courpus, f)
'''
Out[31]:
In [32]:
# # stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/
# # make sure you have the glove_vectors file
# with open('glove_vectors', 'rb') as f:
# model = pickle.load(f)
# glove_words = set(model.keys())
In [33]:
# # average Word2Vec
# # compute average word2vec for each review.
# avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
# for sentence in tqdm(preprocessed_essays): # for each review/sentence
# vector = np.zeros(300) # as word vectors are of zero length
# cnt_words =0; # num of words with a valid vector in the sentence/review
# for word in sentence.split(): # for each word in a review/sentence
# if word in glove_words:
# vector += model[word]
# cnt_words += 1
# if cnt_words != 0:
# vector /= cnt_words
# avg_w2v_vectors.append(vector)
# print(len(avg_w2v_vectors))
# print(len(avg_w2v_vectors[0]))
1.5.2.3 Using Pretrained Models: TFIDF weighted W2V¶
In [34]:
# # S = ["abc def pqr", "def def def abc", "pqr pqr def"]
# tfidf_model = TfidfVectorizer()
# tfidf_model.fit(preprocessed_essays)
# # we are converting a dictionary with word as a key, and the idf as a value
# dictionary = dict(zip(tfidf_model.get_feature_names(), list(tfidf_model.idf_)))
# tfidf_words = set(tfidf_model.get_feature_names())
In [35]:
# # average Word2Vec
# # compute average word2vec for each review.
# tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
# for sentence in tqdm(preprocessed_essays): # for each review/sentence
# vector = np.zeros(300) # as word vectors are of zero length
# tf_idf_weight =0; # num of words with a valid vector in the sentence/review
# for word in sentence.split(): # for each word in a review/sentence
# if (word in glove_words) and (word in tfidf_words):
# vec = model[word] # getting the vector for each word
# # here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
# tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
# vector += (vec * tf_idf) # calculating tfidf weighted w2v
# tf_idf_weight += tf_idf
# if tf_idf_weight != 0:
# vector /= tf_idf_weight
# tfidf_w2v_vectors.append(vector)
# print(len(tfidf_w2v_vectors))
# print(len(tfidf_w2v_vectors[0]))
In [36]:
# Similarly you can vectorize for title also
1.5.3 Vectorizing Numerical features¶
In [37]:
# price_data = resource_data.groupby('id').agg({'price':'sum', 'quantity':'sum'}).reset_index()
# project_data = pd.merge(project_data, price_data, on='id', how='left')
In [38]:
# # check this one: https://www.youtube.com/watch?v=0HOqOcln3Z4&t=530s
# # standardization sklearn: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
# from sklearn.preprocessing import StandardScaler
# # price_standardized = standardScalar.fit(project_data['price'].values)
# # this will rise the error
# # ValueError: Expected 2D array, got 1D array instead: array=[725.05 213.03 329. ... 399. 287.73 5.5 ].
# # Reshape your data either using array.reshape(-1, 1)
# price_scalar = StandardScaler()
# price_scalar.fit(project_data['price'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
# print(f"Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# # Now standardize the data with above maen and variance.
# price_standardized = price_scalar.transform(project_data['price'].values.reshape(-1, 1))
In [39]:
# price_standardized
1.5.4 Merging all the above features¶
- we need to merge all the numerical vectors i.e catogorical, text, numerical vectors
In [40]:
# print(categories_one_hot.shape)
# # print(sub_categories_one_hot.shape)
# print(text_bow.shape)
# print(price_standardized.shape)
In [41]:
# # merge two sparse matrices: https://stackoverflow.com/a/19710648/4084039
# from scipy.sparse import hstack
# # with the same hstack function we are concatinating a sparse matrix and a dense matirx :)
# X = hstack((categories_one_hot, sub_categories_one_hot, text_bow, price_standardized))
# X.shape
In [42]:
# please write all the code with proper documentation, and proper titles for each subsection
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
Computing Sentiment Scores
In [43]:
import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer
# import nltk
# nltk.download('vader_lexicon')
sid = SentimentIntensityAnalyzer()
for_sentiment = 'a person is a person no matter how small dr seuss i teach the smallest students with the biggest enthusiasm \
for learning my students learn in many different ways using all of our senses and multiple intelligences i use a wide range\
of techniques to help all my students succeed students in my class come from a variety of different backgrounds which makes\
for wonderful sharing of experiences and cultures including native americans our school is a caring community of successful \
learners which can be seen through collaborative student project based learning in and out of the classroom kindergarteners \
in my class love to work with hands on materials and have many different opportunities to practice a skill before it is\
mastered having the social skills to work cooperatively with friends is a crucial aspect of the kindergarten curriculum\
montana is the perfect place to learn about agriculture and nutrition my students love to role play in our pretend kitchen\
in the early childhood classroom i have had several kids ask me can we try cooking with real food i will take their idea \
and create common core cooking lessons where we learn important math and writing concepts while cooking delicious healthy \
food for snack time my students will have a grounded appreciation for the work that went into making the food and knowledge \
of where the ingredients came from as well as how it is healthy for their bodies this project would expand our learning of \
nutrition and agricultural cooking recipes by having us peel our own apples to make homemade applesauce make our own bread \
and mix up healthy plants from our classroom garden in the spring we will also create our own cookbooks to be printed and \
shared with families students will gain math and literature skills as well as a life long enjoyment for healthy cooking \
nannan'
ss = sid.polarity_scores(for_sentiment)
for k in ss:
print('{0}: {1}, '.format(k, ss[k]), end='')
# we can use these 4 things as features/attributes (neg, neu, pos, compound)
# neg: 0.0, neu: 0.753, pos: 0.247, compound: 0.93
In [44]:
import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer
# import nltk
nltk.download('vader_lexicon')
sid = SentimentIntensityAnalyzer()
sentiment_titles=[]
for sentance in tqdm(project_data['essay'].values):
ss = sid.polarity_scores(sentance)
sentiment_titles.append(ss)
In [45]:
project_data.columns
Out[45]:
In [46]:
sentiment_neg=[]
sentiment_neu=[]
sentiment_pos=[]
sentiment_compound=[]
for i in sentiment_titles:
for j,k in i.items():
if(j=='neg'):
sentiment_neg.append(k)
else:
if(j=='neu'):
sentiment_neu.append(k)
else:
if(j=='pos'):
sentiment_pos.append(k)
else:
if(j=='compound'):
sentiment_compound.append(k)
In [47]:
project_data['sentiment_neg'] = sentiment_neg
project_data['sentiment_neu'] = sentiment_neu
project_data['sentiment_pos'] = sentiment_pos
project_data['sentiment_compound'] = sentiment_compound
In [48]:
project_data.columns
Out[48]:
In [49]:
#https://stackoverflow.com/questions/49984905/count-number-of-words-per-row
project_data['words_title'] = project_data['project_title'].str.split().str.len()
In [50]:
#https://stackoverflow.com/questions/49984905/count-number-of-words-per-row
project_data['words_essay'] = project_data['essay'].str.split().str.len()
In [51]:
project_data.head(2)
Out[51]:
Assignment 7: SVM¶
- [Task-1] Apply Support Vector Machines(SGDClassifier with hinge loss: Linear SVM) on these feature sets
- Set 1: categorical, numerical features + project_title(BOW) + preprocessed_eassay (BOW)
- Set 2: categorical, numerical features + project_title(TFIDF)+ preprocessed_eassay (TFIDF)
- Set 3: categorical, numerical features + project_title(AVG W2V)+ preprocessed_eassay (AVG W2V)
- Set 4: categorical, numerical features + project_title(TFIDF W2V)+ preprocessed_eassay (TFIDF W2V)
- The hyper paramter tuning (best alpha in range [10^-4 to 10^4], and the best penalty among 'l1', 'l2')
- Find the best hyper parameter which will give the maximum AUC value
- Find the best hyper paramter using k-fold cross validation or simple cross validation data
- Use gridsearch cv or randomsearch cv or you can also write your own for loops to do this task of hyperparameter tuning
- Representation of results
- You need to plot the performance of model both on train data and cross validation data for each hyper parameter, like shown in the figure.
- Once after you found the best hyper parameter, you need to train your model with it, and find the AUC on test data and plot the ROC curve on both train and test.
- Along with plotting ROC curve, you need to print the confusion matrix with predicted and original labels of test data points. Please visualize your confusion matrices using seaborn heatmaps.
- You need to plot the performance of model both on train data and cross validation data for each hyper parameter, like shown in the figure.
- [Task-2] Apply the Support Vector Machines on these features by finding the best hyper paramter as suggested in step 2 and step 3
- Consider these set of features Set 5 :
- school_state : categorical data
- clean_categories : categorical data
- clean_subcategories : categorical data
- project_grade_category :categorical data
- teacher_prefix : categorical data
- quantity : numerical data
- teacher_number_of_previously_posted_projects : numerical data
- price : numerical data
- sentiment score's of each of the essay : numerical data
- number of words in the title : numerical data
- number of words in the combine essays : numerical data
- Apply TruncatedSVD on TfidfVectorizer of essay text, choose the number of components (`n_components`) using elbow method : numerical data
- Conclusion
- You need to summarize the results at the end of the notebook, summarize it in the table format. To print out a table please refer to this prettytable library link
- You need to summarize the results at the end of the notebook, summarize it in the table format. To print out a table please refer to this prettytable library link
- Consider these set of features Set 5 :
Note: Data Leakage
- There will be an issue of data-leakage if you vectorize the entire data and then split it into train/cv/test.
- To avoid the issue of data-leakage, make sure to split your data first and then vectorize it.
- While vectorizing your data, apply the method fit_transform() on you train data, and apply the method transform() on cv/test data.
- For more details please go through this link.
2. Support Vector Machines
2.1 Splitting data into Train and cross validation(or test): Stratified Sampling
In [52]:
#https://stackoverflow.com/questions/26837998/pandas-replace-nan-with-blank-empty-string
project_data['teacher_prefix'] = project_data['teacher_prefix'].fillna('null')
In [53]:
y = project_data['project_is_approved'].values
project_data.drop(['project_is_approved'], axis=1, inplace=True)
X = project_data
In [54]:
# please write all the code with proper documentation, and proper titles for each subsection
# go through documentations and blogs before you start coding
# first figure out what to do, and then think about how to do.
# reading and understanding error messages will be very much helpfull in debugging your code
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
# train test split
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, stratify=y)
X_train, X_cv, y_train, y_cv = train_test_split(X_train, y_train, test_size=0.33, stratify=y_train)
In [55]:
print(X_train.shape, y_train.shape)
print(X_cv.shape, y_cv.shape)
print(X_test.shape, y_test.shape)
print("="*100)
2.2 Make Data Model Ready: encoding numerical, categorical features
In [56]:
# please write all the code with proper documentation, and proper titles for each subsection
# go through documentations and blogs before you start coding
# first figure out what to do, and then think about how to do.
# reading and understanding error messages will be very much helpfull in debugging your code
# make sure you featurize train and test data separatly
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
vectorizer = CountVectorizer()
vectorizer.fit(X_train['clean_categories'].values) # fit has to happen only on train data
# we use the fitted CountVectorizer to convert the text to vector
X_train_clean_cat_ohe = vectorizer.transform(X_train['clean_categories'].values)
X_cv_clean_cat_ohe = vectorizer.transform(X_cv['clean_categories'].values)
X_test_clean_cat_ohe = vectorizer.transform(X_test['clean_categories'].values)
print("After vectorizations")
print(X_train_clean_cat_ohe.shape, y_train.shape)
print(X_cv_clean_cat_ohe.shape, y_cv.shape)
print(X_test_clean_cat_ohe.shape, y_test.shape)
print(vectorizer.get_feature_names())
print("="*100)
In [57]:
#Encoding project_subject_subcategories
vectorizer = CountVectorizer()
vectorizer.fit(X_train['clean_subcategories'].values) # fit has to happen only on train data
# we use the fitted CountVectorizer to convert the text to vector
X_train_clean_subcat_ohe = vectorizer.transform(X_train['clean_subcategories'].values)
X_cv_clean_subcat_ohe = vectorizer.transform(X_cv['clean_subcategories'].values)
X_test_clean_subcat_ohe = vectorizer.transform(X_test['clean_subcategories'].values)
print("After vectorizations")
print(X_train_clean_subcat_ohe.shape, y_train.shape)
print(X_cv_clean_subcat_ohe.shape, y_cv.shape)
print(X_test_clean_subcat_ohe.shape, y_test.shape)
print(vectorizer.get_feature_names())
print("="*100)
In [58]:
#one hot encoding for teacher_prefix
from collections import Counter
my_counter = Counter()
for word in project_data['teacher_prefix'].values:
my_counter.update(str(word).split())
teacher_dict = dict(my_counter)
sorted_teacher_dict = dict(sorted(teacher_dict.items(), key=lambda kv: kv[1]))
print(sorted_teacher_dict)
vectorizer = CountVectorizer(vocabulary=list((sorted_teacher_dict.keys())), lowercase=False, binary=True)
vectorizer.fit(project_data['teacher_prefix'].values)
#Encoding Teacher prefix
# vectorizer = CountVectorizer()
# vectorizer.fit(X_train['teacher_prefix'].values) # fit has to happen only on train data
# we use the fitted CountVectorizer to convert the text to vector
X_train_teacher_ohe = vectorizer.transform(X_train['teacher_prefix'].values)
X_cv_teacher_ohe = vectorizer.transform(X_cv['teacher_prefix'].values)
X_test_teacher_ohe = vectorizer.transform(X_test['teacher_prefix'].values)
print("After vectorizations")
print(X_train_teacher_ohe.shape, y_train.shape)
print(X_cv_teacher_ohe.shape, y_cv.shape)
print(X_test_teacher_ohe.shape, y_test.shape)
print(vectorizer.get_feature_names())
print("="*100)
In [59]:
#Encoding school state
vectorizer = CountVectorizer()
vectorizer.fit(X_train['school_state'].values) # fit has to happen only on train data
# we use the fitted CountVectorizer to convert the text to vector
X_train_state_ohe = vectorizer.transform(X_train['school_state'].values)
X_cv_state_ohe = vectorizer.transform(X_cv['school_state'].values)
X_test_state_ohe = vectorizer.transform(X_test['school_state'].values)
print("After vectorizations")
print(X_train_state_ohe.shape, y_train.shape)
print(X_cv_state_ohe.shape, y_cv.shape)
print(X_test_state_ohe.shape, y_test.shape)
print(vectorizer.get_feature_names())
print("="*100)
In [60]:
from collections import Counter
my_counter = Counter()
for word in X_train['project_grade_category'].values:
my_counter.update(word.split())
# dict sort by value python: https://stackoverflow.com/a/613218/4084039
grade_dict = dict(my_counter)
sorted_grade_dict = dict(sorted(grade_dict.items(), key=lambda kv: kv[1]))
# print(sorted_grade_dict.keys())
In [61]:
#one hot encoding the catogorical features: project_grade_category
vectorizer = CountVectorizer(vocabulary=list(sorted_grade_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(X_train['project_grade_category'].values) # fit has to happen only on train data
# we use the fitted CountVectorizer to convert the text to vector
X_train_grade_ohe = vectorizer.transform(X_train['project_grade_category'].values)
X_cv_grade_ohe = vectorizer.transform(X_cv['project_grade_category'].values)
X_test_grade_ohe = vectorizer.transform(X_test['project_grade_category'].values)
print("After vectorizations")
print(X_train_grade_ohe.shape, y_train.shape)
print(X_cv_grade_ohe.shape, y_cv.shape)
print(X_test_grade_ohe.shape, y_test.shape)
print(vectorizer.get_feature_names())
print("="*100)
In [62]:
#Normalising the numerical feature
from sklearn.preprocessing import StandardScaler
standard_vec = StandardScaler(with_mean = False)
# normalizer.fit(X_train['price'].values)
# this will rise an error Expected 2D array, got 1D array instead:
# array=[105.22 215.96 96.01 ... 368.98 80.53 709.67].
# Reshape your data either using
# array.reshape(-1, 1) if your data has a single feature
# array.reshape(1, -1) if it contains a single sample.
standard_vec.fit(X_train['price'].values.reshape(-1,1))
X_train_price_std = standard_vec.transform(X_train['price'].values.reshape(-1,1))
X_cv_price_std = standard_vec.transform(X_cv['price'].values.reshape(-1,1))
X_test_price_std = standard_vec.transform(X_test['price'].values.reshape(-1,1))
print("After vectorizations")
print(X_train_price_std.shape, y_train.shape)
print(X_cv_price_std.shape, y_cv.shape)
print(X_test_price_std.shape, y_test.shape)
print("="*100)
In [63]:
#Normalising the numerical feature-no of words in essay
from sklearn.preprocessing import StandardScaler
standard_vec = StandardScaler(with_mean = False)
# normalizer.fit(X_train['price'].values)
# this will rise an error Expected 2D array, got 1D array instead:
# array=[105.22 215.96 96.01 ... 368.98 80.53 709.67].
# Reshape your data either using
# array.reshape(-1, 1) if your data has a single feature
# array.reshape(1, -1) if it contains a single sample.
standard_vec.fit(X_train['words_essay'].values.reshape(-1,1))
X_train_words_essay_std = standard_vec.transform(X_train['words_essay'].values.reshape(-1,1))
X_cv_words_essay_std = standard_vec.transform(X_cv['words_essay'].values.reshape(-1,1))
X_test_words_essay_std = standard_vec.transform(X_test['words_essay'].values.reshape(-1,1))
print("After vectorizations")
print(X_train_words_essay_std.shape, y_train.shape)
print(X_cv_words_essay_std.shape, y_cv.shape)
print(X_test_words_essay_std.shape, y_test.shape)
print("="*100)
In [64]:
#Normalising the numerical feature-no of words in titles
from sklearn.preprocessing import StandardScaler
standard_vec = StandardScaler(with_mean = False)
# normalizer.fit(X_train['price'].values)
# this will rise an error Expected 2D array, got 1D array instead:
# array=[105.22 215.96 96.01 ... 368.98 80.53 709.67].
# Reshape your data either using
# array.reshape(-1, 1) if your data has a single feature
# array.reshape(1, -1) if it contains a single sample.
standard_vec.fit(X_train['words_title'].values.reshape(-1,1))
X_train_words_title_std = standard_vec.transform(X_train['words_title'].values.reshape(-1,1))
X_cv_words_title_std = standard_vec.transform(X_cv['words_title'].values.reshape(-1,1))
X_test_words_title_std = standard_vec.transform(X_test['words_title'].values.reshape(-1,1))
print("After vectorizations")
print(X_train_words_title_std.shape, y_train.shape)
print(X_cv_words_title_std.shape, y_cv.shape)
print(X_test_words_title_std.shape, y_test.shape)
print("="*100)
In [65]:
#Normalising the numerical feature-sentiment score of neg words
from sklearn.preprocessing import StandardScaler
standard_vec = StandardScaler(with_mean = False)
# normalizer.fit(X_train['price'].values)
# this will rise an error Expected 2D array, got 1D array instead:
# array=[105.22 215.96 96.01 ... 368.98 80.53 709.67].
# Reshape your data either using
# array.reshape(-1, 1) if your data has a single feature
# array.reshape(1, -1) if it contains a single sample.
standard_vec.fit(X_train['sentiment_neg'].values.reshape(-1,1))
X_train_sentiment_neg_std = standard_vec.transform(X_train['sentiment_neg'].values.reshape(-1,1))
X_cv_sentiment_neg_std = standard_vec.transform(X_cv['sentiment_neg'].values.reshape(-1,1))
X_test_sentiment_neg_std = standard_vec.transform(X_test['sentiment_neg'].values.reshape(-1,1))
print("After vectorizations")
print(X_train_sentiment_neg_std.shape, y_train.shape)
print(X_cv_sentiment_neg_std.shape, y_cv.shape)
print(X_test_sentiment_neg_std.shape, y_test.shape)
print("="*100)
In [66]:
#Normalising the numerical feature-sentiment score of neutral words
from sklearn.preprocessing import StandardScaler
standard_vec = StandardScaler(with_mean = False)
# normalizer.fit(X_train['price'].values)
# this will rise an error Expected 2D array, got 1D array instead:
# array=[105.22 215.96 96.01 ... 368.98 80.53 709.67].
# Reshape your data either using
# array.reshape(-1, 1) if your data has a single feature
# array.reshape(1, -1) if it contains a single sample.
standard_vec.fit(X_train['sentiment_neu'].values.reshape(-1,1))
X_train_sentiment_neu_std = standard_vec.transform(X_train['sentiment_neu'].values.reshape(-1,1))
X_cv_sentiment_neu_std = standard_vec.transform(X_cv['sentiment_neu'].values.reshape(-1,1))
X_test_sentiment_neu_std = standard_vec.transform(X_test['sentiment_neu'].values.reshape(-1,1))
print("After vectorizations")
print(X_train_sentiment_neu_std.shape, y_train.shape)
print(X_cv_sentiment_neu_std.shape, y_cv.shape)
print(X_test_sentiment_neu_std.shape, y_test.shape)
print("="*100)
In [67]:
#Normalising the numerical feature-sentiment score of positive words
from sklearn.preprocessing import StandardScaler
standard_vec = StandardScaler(with_mean = False)
# normalizer.fit(X_train['price'].values)
# this will rise an error Expected 2D array, got 1D array instead:
# array=[105.22 215.96 96.01 ... 368.98 80.53 709.67].
# Reshape your data either using
# array.reshape(-1, 1) if your data has a single feature
# array.reshape(1, -1) if it contains a single sample.
standard_vec.fit(X_train['sentiment_pos'].values.reshape(-1,1))
X_train_sentiment_pos_std = standard_vec.transform(X_train['sentiment_pos'].values.reshape(-1,1))
X_cv_sentiment_pos_std = standard_vec.transform(X_cv['sentiment_pos'].values.reshape(-1,1))
X_test_sentiment_pos_std = standard_vec.transform(X_test['sentiment_pos'].values.reshape(-1,1))
print("After vectorizations")
print(X_train_sentiment_pos_std.shape, y_train.shape)
print(X_cv_sentiment_pos_std.shape, y_cv.shape)
print(X_test_sentiment_pos_std.shape, y_test.shape)
print("="*100)
In [68]:
#Normalising the numerical feature-sentiment score of compound words
from sklearn.preprocessing import StandardScaler
standard_vec = StandardScaler(with_mean = False)
# normalizer.fit(X_train['price'].values)
# this will rise an error Expected 2D array, got 1D array instead:
# array=[105.22 215.96 96.01 ... 368.98 80.53 709.67].
# Reshape your data either using
# array.reshape(-1, 1) if your data has a single feature
# array.reshape(1, -1) if it contains a single sample.
standard_vec.fit(X_train['sentiment_compound'].values.reshape(-1,1))
X_train_sentiment_compound_std = standard_vec.transform(X_train['sentiment_compound'].values.reshape(-1,1))
X_cv_sentiment_compound_std = standard_vec.transform(X_cv['sentiment_compound'].values.reshape(-1,1))
X_test_sentiment_compound_std = standard_vec.transform(X_test['sentiment_compound'].values.reshape(-1,1))
print("After vectorizations")
print(X_train_sentiment_compound_std.shape, y_train.shape)
print(X_cv_sentiment_compound_std.shape, y_cv.shape)
print(X_test_sentiment_compound_std.shape, y_test.shape)
print("="*100)
In [69]:
#Normalising the numerical features: teacher_number_of_previously _posted_projects
from sklearn.preprocessing import StandardScaler
standard_vec = StandardScaler(with_mean = False)
# normalizer.fit(X_train['price'].values)
# this will rise an error Expected 2D array, got 1D array instead:
# array=[105.22 215.96 96.01 ... 368.98 80.53 709.67].
# Reshape your data either using
# array.reshape(-1, 1) if your data has a single feature
# array.reshape(1, -1) if it contains a single sample.
standard_vec.fit(X_train['teacher_number_of_previously_posted_projects'].values.reshape(-1,1))
X_train_projects_std = standard_vec.transform(X_train['teacher_number_of_previously_posted_projects'].values.reshape(-1,1))
X_cv_projects_std = standard_vec.transform(X_cv['teacher_number_of_previously_posted_projects'].values.reshape(-1,1))
X_test_projects_std = standard_vec.transform(X_test['teacher_number_of_previously_posted_projects'].values.reshape(-1,1))
print("After vectorizations")
print(X_train_projects_std.shape, y_train.shape)
print(X_cv_projects_std.shape, y_cv.shape)
print(X_test_projects_std.shape, y_test.shape)
print("="*100)
In [70]:
#Normalising numerical features: "quantity
from sklearn.preprocessing import StandardScaler
standard_vec = StandardScaler(with_mean = False)
# this will rise an error Expected 2D array, got 1D array instead:
# array=[105.22 215.96 96.01 ... 368.98 80.53 709.67].
# Reshape your data either using
# array.reshape(-1, 1) if your data has a single feature
# array.reshape(1, -1) if it contains a single sample.
standard_vec.fit(X_train['quantity'].values.reshape(-1,1))
X_train_qty_std = standard_vec.transform(X_train['quantity'].values.reshape(-1,1))
X_cv_qty_std = standard_vec.transform(X_cv['quantity'].values.reshape(-1,1))
X_test_qty_std = standard_vec.transform(X_test['quantity'].values.reshape(-1,1))
print("After vectorizations")
print(X_train_qty_std.shape, y_train.shape)
print(X_cv_qty_std.shape, y_cv.shape)
print(X_test_qty_std.shape, y_test.shape)
print("="*100)
2.3 Make Data Model Ready: encoding eassay, and project_title
In [71]:
# please write all the code with proper documentation, and proper titles for each subsection
# go through documentations and blogs before you start coding
# first figure out what to do, and then think about how to do.
# reading and understanding error messages will be very much helpfull in debugging your code
# make sure you featurize train and test data separatly
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(min_df=10,ngram_range=(1,2), max_features=5000)
vectorizer.fit(X_train['essay'].values) # fit has to happen only on train data
# we use the fitted CountVectorizer to convert the text to vector
X_train_essay_bow = vectorizer.transform(X_train['clean_essays'].values)
X_cv_essay_bow = vectorizer.transform(X_cv['clean_essays'].values)
X_test_essay_bow = vectorizer.transform(X_test['clean_essays'].values)
print("After vectorizations")
print(X_train_essay_bow.shape, y_train.shape)
print(X_cv_essay_bow.shape, y_cv.shape)
print(X_test_essay_bow.shape, y_test.shape)
print("="*100)
In [72]:
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(min_df=10,ngram_range=(1,2), max_features=5000)
vectorizer.fit(X_train['clean_titles'].values) # fit has to happen only on train data
# we use the fitted CountVectorizer to convert the text to vector
X_train_titles_bow = vectorizer.transform(X_train['clean_titles'].values)
X_cv_titles_bow = vectorizer.transform(X_cv['clean_titles'].values)
X_test_titles_bow = vectorizer.transform(X_test['clean_titles'].values)
print("After vectorizations")
print(X_train_titles_bow.shape, y_train.shape)
print(X_cv_titles_bow.shape, y_cv.shape)
print(X_test_titles_bow.shape, y_test.shape)
print("="*100)
In [73]:
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(min_df=10,ngram_range=(1,2), max_features=5000)
vectorizer.fit(X_train['project_resource_summary'].values) # fit has to happen only on train data
# we use the fitted CountVectorizer to convert the text to vector
X_train_summary_bow = vectorizer.transform(X_train['project_resource_summary'].values)
X_cv_summary_bow = vectorizer.transform(X_cv['project_resource_summary'].values)
X_test_summary_bow = vectorizer.transform(X_test['project_resource_summary'].values)
print("After vectorizations")
print(X_train_summary_bow.shape, y_train.shape)
print(X_cv_summary_bow.shape, y_cv.shape)
print(X_test_summary_bow.shape, y_test.shape)
print("="*100)
In [74]:
# merge two sparse matrices: https://stackoverflow.com/a/19710648/4084039
from scipy.sparse import hstack
X_tr = hstack((X_train_essay_bow,X_train_titles_bow,X_train_summary_bow,X_train_clean_cat_ohe,X_train_clean_subcat_ohe, X_train_state_ohe, X_train_teacher_ohe, X_train_grade_ohe, X_train_price_std,X_train_projects_std,X_train_qty_std)).tocsr()
X_cr = hstack((X_cv_essay_bow,X_cv_titles_bow,X_cv_summary_bow,X_cv_clean_cat_ohe,X_cv_clean_subcat_ohe, X_cv_state_ohe, X_cv_teacher_ohe, X_cv_grade_ohe, X_cv_price_std,X_cv_projects_std,X_cv_qty_std)).tocsr()
X_te = hstack((X_test_essay_bow,X_test_titles_bow,X_test_summary_bow,X_test_clean_cat_ohe,X_test_clean_subcat_ohe, X_test_state_ohe, X_test_teacher_ohe, X_test_grade_ohe, X_test_price_std,X_test_projects_std,X_test_qty_std)).tocsr()
print("Final Data matrix")
print(X_tr.shape, y_train.shape)
print(X_cr.shape, y_cv.shape)
print(X_te.shape, y_test.shape)
print("="*100)
In [75]:
def batch_predict(clf, data):
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
y_data_pred = []
tr_loop = data.shape[0] - data.shape[0]%1000
# consider you X_tr shape is 49041, then your cr_loop will be 49041 - 49041%1000 = 49000
# in this for loop we will iterate unti the last 1000 multiplier
for i in range(0, tr_loop, 1000):
y_data_pred.extend(clf.predict_proba(data[i:i+1000])[:,1])
# we will be predicting for the last data points
y_data_pred.extend(clf.predict_proba(data[tr_loop:])[:,1])
return y_data_pred
In [76]:
%%time
# https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import SGDClassifier
sgd=SGDClassifier()
tuned_parameters = {'alpha': [10**-3, 10**-2,10**-1, 10**0]}
clf = GridSearchCV(sgd, tuned_parameters, cv=3, scoring='roc_auc')
clf.fit(X_tr, y_train)
train_auc= clf.cv_results_['mean_train_score']
train_auc_std= clf.cv_results_['std_train_score']
cv_auc = clf.cv_results_['mean_test_score']
cv_auc_std= clf.cv_results_['std_test_score']
plt.plot(tuned_parameters['alpha'], train_auc, label='Train AUC')
# this code is copied from here: https://stackoverflow.com/a/48803361/4084039
plt.gca().fill_between(tuned_parameters['alpha'],train_auc - train_auc_std,train_auc + train_auc_std,alpha=0.2,color='darkblue')
plt.plot(tuned_parameters['alpha'], cv_auc, label='CV AUC')
# this code is copied from here: https://stackoverflow.com/a/48803361/4084039
plt.gca().fill_between(tuned_parameters['alpha'],cv_auc - cv_auc_std,cv_auc + cv_auc_std,alpha=0.2,color='darkorange')
plt.scatter(tuned_parameters['alpha'], train_auc, label='Train AUC points')
plt.scatter(tuned_parameters['alpha'], cv_auc, label='CV AUC points')
plt.legend()
plt.xlabel("alpha: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
# print(clf.best_estimator_)
In [77]:
%%time
# https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import SGDClassifier
sgd=SGDClassifier(penalty='l1')
tuned_parameters = {'alpha': [10**-3, 10**-2,10**-1]}
clf = GridSearchCV(sgd, tuned_parameters, cv=3, scoring='roc_auc')
clf.fit(X_tr, y_train)
train_auc= clf.cv_results_['mean_train_score']
train_auc_std= clf.cv_results_['std_train_score']
cv_auc = clf.cv_results_['mean_test_score']
cv_auc_std= clf.cv_results_['std_test_score']
plt.plot(tuned_parameters['alpha'], train_auc, label='Train AUC')
# this code is copied from here: https://stackoverflow.com/a/48803361/4084039
plt.gca().fill_between(tuned_parameters['alpha'],train_auc - train_auc_std,train_auc + train_auc_std,alpha=0.2,color='darkblue')
plt.plot(tuned_parameters['alpha'], cv_auc, label='CV AUC')
# this code is copied from here: https://stackoverflow.com/a/48803361/4084039
plt.gca().fill_between(tuned_parameters['alpha'],cv_auc - cv_auc_std,cv_auc + cv_auc_std,alpha=0.2,color='darkorange')
plt.scatter(tuned_parameters['alpha'], train_auc, label='Train AUC points')
plt.scatter(tuned_parameters['alpha'], cv_auc, label='CV AUC points')
plt.legend()
plt.xlabel("alpha: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
# print(clf.best_estimator_)
L2 regulizer seems to be more apt.since AUC for L2>AUC for L1.
In [78]:
%%time
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve
from sklearn.metrics import roc_curve, auc
from sklearn.linear_model import LogisticRegression
from sklearn.calibration import CalibratedClassifierCV
sgd=SGDClassifier(alpha=0.01)
#https://github.com/scikit-learn/scikit-learn/issues/7278
calibrated_clf = CalibratedClassifierCV(sgd, method='sigmoid')
calibrated_clf.fit(X_tr, y_train)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
y_train_pred = batch_predict(calibrated_clf, X_tr)
y_test_pred = batch_predict(calibrated_clf, X_te)
train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)
plt.plot(train_fpr, train_tpr, label="train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("alpha: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
In [79]:
# we are writing our own function for predict, with defined thresould
# we will pick a threshold that will give the least fpr
def predict(proba, threshould, fpr, tpr):
t = threshould[np.argmax(fpr*(1-tpr))]
# (tpr*(1-fpr)) will be maximum if your fpr is very low and tpr is very high
print("the maximum value of tpr*(1-fpr)", max(tpr*(1-fpr)), "for threshold", np.round(t,3))
predictions = []
for i in proba:
if i>=t:
predictions.append(1)
else:
predictions.append(0)
return predictions
In [80]:
print("="*100)
from sklearn.metrics import confusion_matrix
print("Train confusion matrix")
print(confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_fpr)))
print("Test confusion matrix")
print(confusion_matrix(y_test, predict(y_test_pred, tr_thresholds, test_fpr, test_fpr)))
In [81]:
#https://seaborn.pydata.org/generated/seaborn.heatmap.html
# import seaborn as sns;
df_cm = pd.DataFrame(confusion_matrix(y_test, predict(y_test_pred, tr_thresholds, test_fpr, test_fpr)))
sns.set(font_scale=1.4)#for label size
sns.heatmap(df_cm, annot=True,annot_kws={"size": 16}, fmt='g')
Out[81]:
In [82]:
#Vectorizing Essays
# Please write all the code with proper documentation
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(min_df=10,ngram_range=(1, 2),max_features=5000)
vectorizer.fit(X_train['clean_essays'].values) # fit has to happen only on train data
# we use the fitted CountVectorizer to convert the text to vector
X_train_essay_tfidf = vectorizer.transform(X_train['clean_essays'].values)
X_cv_essay_tfidf = vectorizer.transform(X_cv['clean_essays'].values)
X_test_essay_tfidf = vectorizer.transform(X_test['clean_essays'].values)
# print(X_train_essay_tfidf.shape)
# print(X_train_essay_tfidf.shape)
# print(X_train_essay_tfidf.shape)
In [83]:
#Vectorizing Titles
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_selection import SelectKBest, chi2
vectorizer = TfidfVectorizer(min_df=10,ngram_range=(1, 2))
vectorizer.fit(X_train['clean_titles'].values) # fit has to happen only on train data
# we use the fitted CountVectorizer to convert the text to vector
X_train_titles_tfidf = vectorizer.transform(X_train['clean_titles'].values)
X_cv_titles_tfidf = vectorizer.transform(X_cv['clean_titles'].values)
X_test_titles_tfidf = vectorizer.transform(X_test['clean_titles'].values)
print("Train shape:",X_train_titles_tfidf.shape)
print("CV shape:",X_cv_titles_tfidf.shape)
print("Test shape:",X_test_titles_tfidf.shape)
In [84]:
# merge two sparse matrices: https://stackoverflow.com/a/19710648/4084039
from scipy.sparse import hstack
X_tr = hstack((X_train_essay_tfidf,X_train_titles_tfidf,X_train_summary_bow,X_train_clean_cat_ohe,X_train_clean_subcat_ohe, X_train_state_ohe, X_train_teacher_ohe, X_train_grade_ohe, X_train_price_std,X_train_projects_std,X_train_qty_std)).tocsr()
X_cr = hstack((X_cv_essay_tfidf,X_cv_titles_tfidf,X_cv_summary_bow,X_cv_clean_cat_ohe,X_cv_clean_subcat_ohe, X_cv_state_ohe, X_cv_teacher_ohe, X_cv_grade_ohe, X_cv_price_std,X_cv_projects_std,X_cv_qty_std)).tocsr()
X_te = hstack((X_test_essay_tfidf,X_test_titles_tfidf,X_test_summary_bow,X_test_clean_cat_ohe,X_test_clean_subcat_ohe, X_test_state_ohe, X_test_teacher_ohe, X_test_grade_ohe, X_test_price_std,X_test_projects_std,X_test_qty_std)).tocsr()
print("Final Data matrix")
print(X_tr.shape, y_train.shape)
print(X_cr.shape, y_cv.shape)
print(X_te.shape, y_test.shape)
print("="*100)
In [85]:
def batch_predict(clf, data):
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
y_data_pred = []
tr_loop = data.shape[0] - data.shape[0]%1000
# consider you X_tr shape is 49041, then your cr_loop will be 49041 - 49041%1000 = 49000
# in this for loop we will iterate unti the last 1000 multiplier
for i in range(0, tr_loop, 1000):
y_data_pred.extend(clf.predict_proba(data[i:i+1000])[:,1])
# we will be predicting for the last data points
y_data_pred.extend(clf.predict_proba(data[tr_loop:])[:,1])
return y_data_pred
In [86]:
%%time
# https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import SGDClassifier
sgd=SGDClassifier()
tuned_parameters = {'alpha': [10**-3, 10**-2,10**-1, 10**0]}
clf = GridSearchCV(sgd, tuned_parameters, cv=3, scoring='roc_auc')
clf.fit(X_tr, y_train)
train_auc= clf.cv_results_['mean_train_score']
train_auc_std= clf.cv_results_['std_train_score']
cv_auc = clf.cv_results_['mean_test_score']
cv_auc_std= clf.cv_results_['std_test_score']
plt.plot(tuned_parameters['alpha'], train_auc, label='Train AUC')
# this code is copied from here: https://stackoverflow.com/a/48803361/4084039
plt.gca().fill_between(tuned_parameters['alpha'],train_auc - train_auc_std,train_auc + train_auc_std,alpha=0.2,color='darkblue')
plt.plot(tuned_parameters['alpha'], cv_auc, label='CV AUC')
# this code is copied from here: https://stackoverflow.com/a/48803361/4084039
plt.gca().fill_between(tuned_parameters['alpha'],cv_auc - cv_auc_std,cv_auc + cv_auc_std,alpha=0.2,color='darkorange')
plt.scatter(tuned_parameters['alpha'], train_auc, label='Train AUC points')
plt.scatter(tuned_parameters['alpha'], cv_auc, label='CV AUC points')
plt.legend()
plt.xlabel("alpha: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
# print(clf.best_estimator_)
In [87]:
%%time
# https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import SGDClassifier
sgd=SGDClassifier(penalty='l1')
tuned_parameters = {'alpha': [10**-3, 10**-2,10**-1]}
clf = GridSearchCV(sgd, tuned_parameters, cv=3, scoring='roc_auc')
clf.fit(X_tr, y_train)
train_auc= clf.cv_results_['mean_train_score']
train_auc_std= clf.cv_results_['std_train_score']
cv_auc = clf.cv_results_['mean_test_score']
cv_auc_std= clf.cv_results_['std_test_score']
plt.plot(tuned_parameters['alpha'], train_auc, label='Train AUC')
# this code is copied from here: https://stackoverflow.com/a/48803361/4084039
plt.gca().fill_between(tuned_parameters['alpha'],train_auc - train_auc_std,train_auc + train_auc_std,alpha=0.2,color='darkblue')
plt.plot(tuned_parameters['alpha'], cv_auc, label='CV AUC')
# this code is copied from here: https://stackoverflow.com/a/48803361/4084039
plt.gca().fill_between(tuned_parameters['alpha'],cv_auc - cv_auc_std,cv_auc + cv_auc_std,alpha=0.2,color='darkorange')
plt.scatter(tuned_parameters['alpha'], train_auc, label='Train AUC points')
plt.scatter(tuned_parameters['alpha'], cv_auc, label='CV AUC points')
plt.legend()
plt.xlabel("alpha: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
# print(clf.best_estimator_)
In [88]:
%%time
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve
from sklearn.metrics import roc_curve, auc
from sklearn.linear_model import LogisticRegression
from sklearn.calibration import CalibratedClassifierCV
sgd=SGDClassifier(alpha=0.001)
#https://github.com/scikit-learn/scikit-learn/issues/7278
calibrated_clf = CalibratedClassifierCV(sgd, method='sigmoid')
calibrated_clf.fit(X_tr, y_train)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
y_train_pred = batch_predict(calibrated_clf, X_tr)
y_test_pred = batch_predict(calibrated_clf, X_te)
train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)
plt.plot(train_fpr, train_tpr, label="train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("alpha: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
In [89]:
# we are writing our own function for predict, with defined thresould
# we will pick a threshold that will give the least fpr
def predict(proba, threshould, fpr, tpr):
t = threshould[np.argmax(fpr*(1-tpr))]
# (tpr*(1-fpr)) will be maximum if your fpr is very low and tpr is very high
print("the maximum value of tpr*(1-fpr)", max(tpr*(1-fpr)), "for threshold", np.round(t,3))
predictions = []
for i in proba:
if i>=t:
predictions.append(1)
else:
predictions.append(0)
return predictions
In [90]:
print("="*100)
from sklearn.metrics import confusion_matrix
print("Train confusion matrix")
print(confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_fpr)))
print("Test confusion matrix")
print(confusion_matrix(y_test, predict(y_test_pred, tr_thresholds, test_fpr, test_fpr)))
In [91]:
#https://seaborn.pydata.org/generated/seaborn.heatmap.html
# import seaborn as sns;
df_cm = pd.DataFrame(confusion_matrix(y_test, predict(y_test_pred, tr_thresholds, test_fpr, test_fpr)))
sns.set(font_scale=1.4)#for label size
sns.heatmap(df_cm, annot=True,annot_kws={"size": 16}, fmt='g')
Out[91]:
Linear SVM on SET 3
In [92]:
with open('glove_vectors', 'rb') as f:
model = pickle.load(f)
glove_words = set(model.keys())
# average Word2Vec
# compute average word2vec for each review.
train_w2v_vectors_essays = []; # the avg-w2v for each essay is stored in this list
for sentence in tqdm(X_train['clean_essays'].values): # for each essay in training data
vector = np.zeros(300) # as word vectors are of zero length
cnt_words =0; # num of words with a valid vector in the essay
for word in sentence.split(): # for each word in a essay
if word in glove_words:
vector += model[word]
cnt_words += 1
if cnt_words != 0:
vector /= cnt_words
train_w2v_vectors_essays.append(vector)
print("train vector")
print(len(train_w2v_vectors_essays))
print(len(train_w2v_vectors_essays[0]))
print('='*50)
# average Word2Vec
# compute average word2vec for each review.
test_w2v_vectors_essays = []; # the avg-w2v for each essay is stored in this list
for sentence in tqdm(X_test['clean_essays'].values): # for each essay in training data
vector = np.zeros(300) # as word vectors are of zero length
cnt_words =0; # num of words with a valid vector in the essay
for word in sentence.split(): # for each word in a essay
if word in glove_words:
vector += model[word]
cnt_words += 1
if cnt_words != 0:
vector /= cnt_words
test_w2v_vectors_essays.append(vector)
print("Test vec")
print(len(test_w2v_vectors_essays))
print(len(test_w2v_vectors_essays[0]))
print('='*50)
# average Word2Vec
# compute average word2vec for each review.
cv_w2v_vectors_essays = []; # the avg-w2v for each essay is stored in this list
for sentence in tqdm(X_cv['clean_essays'].values): # for each essay in training data
vector = np.zeros(300) # as word vectors are of zero length
cnt_words =0; # num of words with a valid vector in the essay
for word in sentence.split(): # for each word in a essay
if word in glove_words:
vector += model[word]
cnt_words += 1
if cnt_words != 0:
vector /= cnt_words
cv_w2v_vectors_essays.append(vector)
print("CV vec")
print(len(cv_w2v_vectors_essays))
print(len(cv_w2v_vectors_essays[0]))
print('='*50)
In [93]:
# Changing list to numpy arrays
train_w2v_vectors_essays = np.array(train_w2v_vectors_essays)
test_w2v_vectors_essays = np.array(test_w2v_vectors_essays)
cv_w2v_vectors_essays = np.array(cv_w2v_vectors_essays)
In [94]:
# average Word2Vec
# compute average word2vec for each title.
train_w2v_vectors_titles = []; # the avg-w2v for each essay is stored in this list
for sentence in tqdm(X_train['clean_titles'].values): # for each essay in training data
vector = np.zeros(300) # as word vectors are of zero length
cnt_words =0; # num of words with a valid vector in the essay
for word in sentence.split(): # for each word in a essay
if word in glove_words:
vector += model[word]
cnt_words += 1
if cnt_words != 0:
vector /= cnt_words
train_w2v_vectors_titles.append(vector)
print("train vector")
print(len(train_w2v_vectors_titles))
print(len(train_w2v_vectors_titles[0]))
print('='*50)
# average Word2Vec
# compute average word2vec for each title.
test_w2v_vectors_titles = []; # the avg-w2v for each essay is stored in this list
for sentence in tqdm(X_test['clean_titles'].values): # for each essay in training data
vector = np.zeros(300) # as word vectors are of zero length
cnt_words =0; # num of words with a valid vector in the essay
for word in sentence.split(): # for each word in a essay
if word in glove_words:
vector += model[word]
cnt_words += 1
if cnt_words != 0:
vector /= cnt_words
test_w2v_vectors_titles.append(vector)
print("Test vec")
print(len(test_w2v_vectors_titles))
print(len(test_w2v_vectors_titles[0]))
print('='*50)
# average Word2Vec
# compute average word2vec for each title.
cv_w2v_vectors_titles = []; # the avg-w2v for each essay is stored in this list
for sentence in tqdm(X_cv['clean_titles'].values): # for each essay in training data
vector = np.zeros(300) # as word vectors are of zero length
cnt_words =0; # num of words with a valid vector in the essay
for word in sentence.split(): # for each word in a essay
if word in glove_words:
vector += model[word]
cnt_words += 1
if cnt_words != 0:
vector /= cnt_words
cv_w2v_vectors_titles.append(vector)
print("CV vec")
print(len(cv_w2v_vectors_titles))
print(len(cv_w2v_vectors_titles[0]))
print('='*50)
In [95]:
# Changing list to numpy arrays
train_w2v_vectors_titles = np.array(train_w2v_vectors_titles)
test_w2v_vectors_titles = np.array(test_w2v_vectors_titles)
cv_w2v_vectors_titles = np.array(cv_w2v_vectors_titles)
In [96]:
# merge two sparse matrices: https://stackoverflow.com/a/19710648/4084039
from scipy.sparse import hstack
X_tr = hstack((train_w2v_vectors_essays,train_w2v_vectors_titles,X_train_clean_cat_ohe,X_train_clean_subcat_ohe, X_train_state_ohe, X_train_teacher_ohe, X_train_grade_ohe, X_train_price_std,X_train_projects_std,X_train_qty_std)).tocsr()
X_cr = hstack((cv_w2v_vectors_essays,cv_w2v_vectors_titles,X_cv_clean_cat_ohe,X_cv_clean_subcat_ohe, X_cv_state_ohe, X_cv_teacher_ohe, X_cv_grade_ohe, X_cv_price_std,X_cv_projects_std,X_cv_qty_std)).tocsr()
X_te = hstack((test_w2v_vectors_essays,test_w2v_vectors_titles,X_test_clean_cat_ohe,X_test_clean_subcat_ohe, X_test_state_ohe, X_test_teacher_ohe, X_test_grade_ohe, X_test_price_std,X_test_projects_std,X_test_qty_std)).tocsr()
print("Final Data matrix")
print(X_tr.shape, y_train.shape)
print(X_cr.shape, y_cv.shape)
print(X_te.shape, y_test.shape)
print("="*100)
In [97]:
def batch_predict(clf, data):
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
y_data_pred = []
tr_loop = data.shape[0] - data.shape[0]%1000
# consider you X_tr shape is 49041, then your cr_loop will be 49041 - 49041%1000 = 49000
# in this for loop we will iterate unti the last 1000 multiplier
for i in range(0, tr_loop, 1000):
y_data_pred.extend(clf.predict_proba(data[i:i+1000])[:,1])
# we will be predicting for the last data points
y_data_pred.extend(clf.predict_proba(data[tr_loop:])[:,1])
return y_data_pred
In [98]:
%%time
# https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import SGDClassifier
sgd=SGDClassifier()
tuned_parameters = {'alpha': [10**-4,10**-3, 10**-2,10**-1]}
clf = GridSearchCV(sgd, tuned_parameters, cv=3, scoring='roc_auc')
clf.fit(X_tr, y_train)
train_auc= clf.cv_results_['mean_train_score']
train_auc_std= clf.cv_results_['std_train_score']
cv_auc = clf.cv_results_['mean_test_score']
cv_auc_std= clf.cv_results_['std_test_score']
plt.plot(tuned_parameters['alpha'], train_auc, label='Train AUC')
# this code is copied from here: https://stackoverflow.com/a/48803361/4084039
plt.gca().fill_between(tuned_parameters['alpha'],train_auc - train_auc_std,train_auc + train_auc_std,alpha=0.2,color='darkblue')
plt.plot(tuned_parameters['alpha'], cv_auc, label='CV AUC')
# this code is copied from here: https://stackoverflow.com/a/48803361/4084039
plt.gca().fill_between(tuned_parameters['alpha'],cv_auc - cv_auc_std,cv_auc + cv_auc_std,alpha=0.2,color='darkorange')
plt.scatter(tuned_parameters['alpha'], train_auc, label='Train AUC points')
plt.scatter(tuned_parameters['alpha'], cv_auc, label='CV AUC points')
plt.legend()
plt.xlabel("alpha: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
# print(clf.best_estimator_)
In [99]:
%%time
# https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import SGDClassifier
sgd=SGDClassifier(penalty='l1')
tuned_parameters = {'alpha': [10**-4,10**-3, 10**-2,10**-1]}
clf = GridSearchCV(sgd, tuned_parameters, cv=3, scoring='roc_auc')
clf.fit(X_tr, y_train)
train_auc= clf.cv_results_['mean_train_score']
train_auc_std= clf.cv_results_['std_train_score']
cv_auc = clf.cv_results_['mean_test_score']
cv_auc_std= clf.cv_results_['std_test_score']
plt.plot(tuned_parameters['alpha'], train_auc, label='Train AUC')
# this code is copied from here: https://stackoverflow.com/a/48803361/4084039
plt.gca().fill_between(tuned_parameters['alpha'],train_auc - train_auc_std,train_auc + train_auc_std,alpha=0.2,color='darkblue')
plt.plot(tuned_parameters['alpha'], cv_auc, label='CV AUC')
# this code is copied from here: https://stackoverflow.com/a/48803361/4084039
plt.gca().fill_between(tuned_parameters['alpha'],cv_auc - cv_auc_std,cv_auc + cv_auc_std,alpha=0.2,color='darkorange')
plt.scatter(tuned_parameters['alpha'], train_auc, label='Train AUC points')
plt.scatter(tuned_parameters['alpha'], cv_auc, label='CV AUC points')
plt.legend()
plt.xlabel("alpha: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
# print(clf.best_estimator_)
L1 is more apt than l2 for this particular case
In [100]:
%%time
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve
from sklearn.metrics import roc_curve, auc
from sklearn.linear_model import LogisticRegression
from sklearn.calibration import CalibratedClassifierCV
sgd=SGDClassifier(alpha=0.0001,penalty='l1')
#https://github.com/scikit-learn/scikit-learn/issues/7278
calibrated_clf = CalibratedClassifierCV(sgd, method='sigmoid')
calibrated_clf.fit(X_tr, y_train)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
y_train_pred = batch_predict(calibrated_clf, X_tr)
y_test_pred = batch_predict(calibrated_clf, X_te)
train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)
plt.plot(train_fpr, train_tpr, label="train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("alpha: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
In [101]:
# we are writing our own function for predict, with defined thresould
# we will pick a threshold that will give the least fpr
def predict(proba, threshould, fpr, tpr):
t = threshould[np.argmax(fpr*(1-tpr))]
# (tpr*(1-fpr)) will be maximum if your fpr is very low and tpr is very high
print("the maximum value of tpr*(1-fpr)", max(tpr*(1-fpr)), "for threshold", np.round(t,3))
predictions = []
for i in proba:
if i>=t:
predictions.append(1)
else:
predictions.append(0)
return predictions
In [102]:
print("="*100)
from sklearn.metrics import confusion_matrix
print("Train confusion matrix")
print(confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_fpr)))
print("Test confusion matrix")
print(confusion_matrix(y_test, predict(y_test_pred, tr_thresholds, test_fpr, test_fpr)))
In [103]:
#https://seaborn.pydata.org/generated/seaborn.heatmap.html
# import seaborn as sns;
df_cm = pd.DataFrame(confusion_matrix(y_test, predict(y_test_pred, tr_thresholds, test_fpr, test_fpr)))
sns.set(font_scale=1.4)#for label size
sns.heatmap(df_cm, annot=True,annot_kws={"size": 16}, fmt='g')
Out[103]:
Linear Regression on Set 4
In [104]:
# Please write all the code with proper documentation
tfidf_model = TfidfVectorizer()
tfidf_model.fit(X_train['clean_essays'].values)
# we are converting a dictionary with word as a key, and the idf as a value
dictionary = dict(zip(tfidf_model.get_feature_names(), list(tfidf_model.idf_)))
tfidf_words = set(tfidf_model.get_feature_names())
# average Word2Vec
# compute average word2vec for each review.
train_tfidf_w2v_essays = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(X_train['clean_essays'].values): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
tf_idf_weight =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if (word in glove_words) and (word in tfidf_words):
vec = model[word] # getting the vector for each word
# here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
vector += (vec * tf_idf) # calculating tfidf weighted w2v
tf_idf_weight += tf_idf
if tf_idf_weight != 0:
vector /= tf_idf_weight
train_tfidf_w2v_essays.append(vector)
print("Train matrix:")
print(len(train_tfidf_w2v_essays))
print(len(train_tfidf_w2v_essays[0]))
print('='*50)
cv_tfidf_w2v_essays = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(X_cv['clean_essays'].values): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
tf_idf_weight =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if (word in glove_words) and (word in tfidf_words):
vec = model[word] # getting the vector for each word
# here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
vector += (vec * tf_idf) # calculating tfidf weighted w2v
tf_idf_weight += tf_idf
if tf_idf_weight != 0:
vector /= tf_idf_weight
cv_tfidf_w2v_essays.append(vector)
print("CV matrix:")
print(len(cv_tfidf_w2v_essays))
print(len(cv_tfidf_w2v_essays[0]))
print('='*50)
test_tfidf_w2v_essays = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(X_test['clean_essays'].values): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
tf_idf_weight =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if (word in glove_words) and (word in tfidf_words):
vec = model[word] # getting the vector for each word
# here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
vector += (vec * tf_idf) # calculating tfidf weighted w2v
tf_idf_weight += tf_idf
if tf_idf_weight != 0:
vector /= tf_idf_weight
test_tfidf_w2v_essays.append(vector)
print("Test matrix:")
print(len(test_tfidf_w2v_essays))
print(len(test_tfidf_w2v_essays[0]))
print('='*50)
In [105]:
# Changing list to numpy arrays
train_tfidf_w2v_essays = np.array(train_tfidf_w2v_essays)
test_tfidf_w2v_essays = np.array(test_tfidf_w2v_essays)
cv_tfidf_w2v_essays = np.array(cv_tfidf_w2v_essays)
In [106]:
tfidf_model = TfidfVectorizer()
tfidf_model.fit(X_train['clean_titles'].values)
# we are converting a dictionary with word as a key, and the idf as a value
dictionary = dict(zip(tfidf_model.get_feature_names(), list(tfidf_model.idf_)))
tfidf_words = set(tfidf_model.get_feature_names())
# average Word2Vec
# compute average word2vec for each review.
train_tfidf_w2v_titles = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(X_train['clean_titles'].values): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
tf_idf_weight =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if (word in glove_words) and (word in tfidf_words):
vec = model[word] # getting the vector for each word
# here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
vector += (vec * tf_idf) # calculating tfidf weighted w2v
tf_idf_weight += tf_idf
if tf_idf_weight != 0:
vector /= tf_idf_weight
train_tfidf_w2v_titles.append(vector)
print("Train matrix:")
print(len(train_tfidf_w2v_titles))
print(len(train_tfidf_w2v_titles[0]))
print('='*50)
cv_tfidf_w2v_titles = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(X_cv['clean_titles'].values): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
tf_idf_weight =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if (word in glove_words) and (word in tfidf_words):
vec = model[word] # getting the vector for each word
# here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
vector += (vec * tf_idf) # calculating tfidf weighted w2v
tf_idf_weight += tf_idf
if tf_idf_weight != 0:
vector /= tf_idf_weight
cv_tfidf_w2v_titles.append(vector)
print("CV matrix:")
print(len(cv_tfidf_w2v_titles))
print(len(cv_tfidf_w2v_titles[0]))
print('='*50)
test_tfidf_w2v_titles = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(X_test['clean_titles'].values): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
tf_idf_weight =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if (word in glove_words) and (word in tfidf_words):
vec = model[word] # getting the vector for each word
# here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
vector += (vec * tf_idf) # calculating tfidf weighted w2v
tf_idf_weight += tf_idf
if tf_idf_weight != 0:
vector /= tf_idf_weight
test_tfidf_w2v_titles.append(vector)
print("Test matrix:")
print(len(test_tfidf_w2v_titles))
print(len(test_tfidf_w2v_titles[0]))
print('='*50)
In [107]:
# Changing list to numpy arrays
train_tfidf_w2v_titles = np.array(train_tfidf_w2v_titles)
test_tfidf_w2v_titles = np.array(test_tfidf_w2v_titles)
cv_tfidf_w2v_titles = np.array(cv_tfidf_w2v_titles)
In [108]:
# merge two sparse matrices: https://stackoverflow.com/a/19710648/4084039
from scipy.sparse import hstack
X_tr = hstack((train_tfidf_w2v_essays,train_tfidf_w2v_titles,X_train_clean_cat_ohe,X_train_clean_subcat_ohe, X_train_state_ohe, X_train_teacher_ohe, X_train_grade_ohe, X_train_price_std,X_train_projects_std,X_train_qty_std)).tocsr()
X_cr = hstack((cv_tfidf_w2v_essays,cv_tfidf_w2v_titles,X_cv_clean_cat_ohe,X_cv_clean_subcat_ohe, X_cv_state_ohe, X_cv_teacher_ohe, X_cv_grade_ohe, X_cv_price_std,X_cv_projects_std,X_cv_qty_std)).tocsr()
X_te = hstack((test_tfidf_w2v_essays,test_tfidf_w2v_titles,X_test_clean_cat_ohe,X_test_clean_subcat_ohe, X_test_state_ohe, X_test_teacher_ohe, X_test_grade_ohe, X_test_price_std,X_test_projects_std,X_test_qty_std)).tocsr()
print("Final Data matrix")
print(X_tr.shape, y_train.shape)
print(X_cr.shape, y_cv.shape)
print(X_te.shape, y_test.shape)
print("="*100)
In [109]:
def batch_predict(clf, data):
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
y_data_pred = []
tr_loop = data.shape[0] - data.shape[0]%1000
# consider you X_tr shape is 49041, then your cr_loop will be 49041 - 49041%1000 = 49000
# in this for loop we will iterate unti the last 1000 multiplier
for i in range(0, tr_loop, 1000):
y_data_pred.extend(clf.predict_proba(data[i:i+1000])[:,1])
# we will be predicting for the last data points
y_data_pred.extend(clf.predict_proba(data[tr_loop:])[:,1])
return y_data_pred
In [110]:
%%time
# https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import SGDClassifier
sgd=SGDClassifier()
tuned_parameters = {'alpha': [10**-4,10**-3, 10**-2,10**-1]}
clf = GridSearchCV(sgd, tuned_parameters, cv=3, scoring='roc_auc')
clf.fit(X_tr, y_train)
train_auc= clf.cv_results_['mean_train_score']
train_auc_std= clf.cv_results_['std_train_score']
cv_auc = clf.cv_results_['mean_test_score']
cv_auc_std= clf.cv_results_['std_test_score']
plt.plot(tuned_parameters['alpha'], train_auc, label='Train AUC')
# this code is copied from here: https://stackoverflow.com/a/48803361/4084039
plt.gca().fill_between(tuned_parameters['alpha'],train_auc - train_auc_std,train_auc + train_auc_std,alpha=0.2,color='darkblue')
plt.plot(tuned_parameters['alpha'], cv_auc, label='CV AUC')
# this code is copied from here: https://stackoverflow.com/a/48803361/4084039
plt.gca().fill_between(tuned_parameters['alpha'],cv_auc - cv_auc_std,cv_auc + cv_auc_std,alpha=0.2,color='darkorange')
plt.scatter(tuned_parameters['alpha'], train_auc, label='Train AUC points')
plt.scatter(tuned_parameters['alpha'], cv_auc, label='CV AUC points')
plt.legend()
plt.xlabel("alpha: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
# print(clf.best_estimator_)
In [111]:
%%time
# https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import SGDClassifier
sgd=SGDClassifier(penalty='l1')
tuned_parameters = {'alpha': [10**-4,10**-4,10**-3]}
clf = GridSearchCV(sgd, tuned_parameters, cv=3, scoring='roc_auc')
clf.fit(X_tr, y_train)
train_auc= clf.cv_results_['mean_train_score']
train_auc_std= clf.cv_results_['std_train_score']
cv_auc = clf.cv_results_['mean_test_score']
cv_auc_std= clf.cv_results_['std_test_score']
plt.plot(tuned_parameters['alpha'], train_auc, label='Train AUC')
# this code is copied from here: https://stackoverflow.com/a/48803361/4084039
plt.gca().fill_between(tuned_parameters['alpha'],train_auc - train_auc_std,train_auc + train_auc_std,alpha=0.2,color='darkblue')
plt.plot(tuned_parameters['alpha'], cv_auc, label='CV AUC')
# this code is copied from here: https://stackoverflow.com/a/48803361/4084039
plt.gca().fill_between(tuned_parameters['alpha'],cv_auc - cv_auc_std,cv_auc + cv_auc_std,alpha=0.2,color='darkorange')
plt.scatter(tuned_parameters['alpha'], train_auc, label='Train AUC points')
plt.scatter(tuned_parameters['alpha'], cv_auc, label='CV AUC points')
plt.legend()
plt.xlabel("alpha: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
# print(clf.best_estimator_)
In [112]:
%%time
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve
from sklearn.metrics import roc_curve, auc
from sklearn.linear_model import LogisticRegression
from sklearn.calibration import CalibratedClassifierCV
sgd=SGDClassifier(alpha=0.0001,penalty='l1')
#https://github.com/scikit-learn/scikit-learn/issues/7278
calibrated_clf = CalibratedClassifierCV(sgd, method='sigmoid')
calibrated_clf.fit(X_tr, y_train)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
y_train_pred = batch_predict(calibrated_clf, X_tr)
y_test_pred = batch_predict(calibrated_clf, X_te)
train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)
plt.plot(train_fpr, train_tpr, label="train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("alpha: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
In [113]:
print("="*100)
from sklearn.metrics import confusion_matrix
print("Train confusion matrix")
print(confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_fpr)))
print("Test confusion matrix")
print(confusion_matrix(y_test, predict(y_test_pred, tr_thresholds, test_fpr, test_fpr)))
In [114]:
#https://seaborn.pydata.org/generated/seaborn.heatmap.html
# import seaborn as sns;
df_cm = pd.DataFrame(confusion_matrix(y_test, predict(y_test_pred, tr_thresholds, test_fpr, test_fpr)))
sns.set(font_scale=1.4)#for label size
sns.heatmap(df_cm, annot=True,annot_kws={"size": 16}, fmt='g')
Out[114]:
2.4 Appling Support Vector Machines on different kind of featurization as mentioned in the instructions
Apply Support Vector Machines on different kind of featurization as mentioned in the instructions
For Every model that you work on make sure you do the step 2 and step 3 of instrucations
In [115]:
# please write all the code with proper documentation, and proper titles for each subsection
# go through documentations and blogs before you start coding
# first figure out what to do, and then think about how to do.
# reading and understanding error messages will be very much helpfull in debugging your code
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
Computing TFIDF vectorizer on Essay data
In [165]:
#Vectorizing Essays
# Please write all the code with proper documentation
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(max_features=5000)
vectorizer.fit(X_train['essay'].values) # fit has to happen only on train data
# we use the fitted CountVectorizer to convert the text to vector
X_train_essay_tsvd_tfidf = vectorizer.transform(X_train['essay'].values)
X_cv_essay_tsvd_tfidf = vectorizer.transform(X_cv['essay'].values)
X_test_essay_tsvd_tfidf = vectorizer.transform(X_test['essay'].values)
print(X_train_essay_tsvd_tfidf.shape)
print(X_cv_essay_tsvd_tfidf.shape)
print(X_test_essay_tsvd_tfidf.shape)
In [166]:
print(type(X_train_essay_tsvd_tfidf))
In [121]:
from sklearn.decomposition import TruncatedSVD
# PCA for dimensionality redcution (non-visualization)
trsvd = TruncatedSVD(n_components=4999)
X_trsvd=trsvd.fit(X_train_essay_tsvd_tfidf)
# print(type(tsvd))
In [128]:
# List of explained variances
from sklearn.decomposition import TruncatedSVD
tsvd_var_ratios = trsvd.explained_variance_ratio_
In [129]:
# Create a function
#https://chrisalbon.com/machine_learning/feature_engineering/select_best_number_of_components_in_tsvd/
def select_n_components(var_ratio, goal_var: float) -> int:
# Set initial variance explained so far
total_variance = 0.0
# Set initial number of features
n_components = 0
# For the explained variance of each feature:
for explained_variance in var_ratio:
# Add the explained variance to the total
total_variance += explained_variance
# Add one to the number of components
n_components += 1
# If we reach our goal level of explained variance
if total_variance >= goal_var:
# End the loop
break
# Return the number of components
return n_components
In [132]:
# Run function
select_n_components(tsvd_var_ratios, 0.90)
Out[132]:
In [133]:
percentage_var_explained = trsvd.explained_variance_ / np.sum(trsvd.explained_variance_);
cum_var_explained = np.cumsum(percentage_var_explained)
# Plot the PCA spectrum
plt.figure(1, figsize=(6, 4))
plt.clf()
plt.plot(cum_var_explained, linewidth=2)
plt.axis('tight')
plt.grid()
plt.xlabel('n_components')
plt.ylabel('Cumulative_explained_variance')
plt.show()
if we take around top 2500 features, we are able to preserve 90% of the variance.
In [167]:
# print(type(X_train_essay_tsvd_tfidf))
X_train_essay_tsvd_tfidf=X_train_essay_tsvd_tfidf[:,0:2500]
X_cv_essay_tsvd_tfidf=X_cv_essay_tsvd_tfidf[:,0:2500]
X_test_essay_tsvd_tfidf=X_test_essay_tsvd_tfidf[:,0:2500]
print(X_test_essay_tsvd_tfidf.shape)
In [168]:
# merge two sparse matrices: https://stackoverflow.com/a/19710648/4084039
from scipy.sparse import hstack
X_tr = hstack((X_train_essay_tsvd_tfidf,X_train_words_essay_std,X_train_words_title_std,X_train_sentiment_neg_std,X_train_sentiment_neu_std,X_train_sentiment_pos_std,X_train_sentiment_compound_std,X_train_clean_cat_ohe,X_train_clean_subcat_ohe, X_train_state_ohe, X_train_teacher_ohe, X_train_grade_ohe, X_train_price_std,X_train_projects_std,X_train_qty_std)).tocsr()
X_cr = hstack((X_cv_essay_tsvd_tfidf,X_cv_words_essay_std,X_cv_words_title_std,X_cv_sentiment_neg_std,X_cv_sentiment_neu_std,X_cv_sentiment_pos_std,X_cv_sentiment_compound_std,X_cv_clean_cat_ohe,X_cv_clean_subcat_ohe, X_cv_state_ohe, X_cv_teacher_ohe, X_cv_grade_ohe, X_cv_price_std,X_cv_projects_std,X_cv_qty_std)).tocsr()
X_te = hstack((X_test_essay_tsvd_tfidf,X_test_words_essay_std,X_test_words_title_std,X_test_sentiment_neg_std,X_test_sentiment_neu_std,X_test_sentiment_pos_std,X_test_sentiment_compound_std,X_test_clean_cat_ohe,X_test_clean_subcat_ohe, X_test_state_ohe, X_test_teacher_ohe, X_test_grade_ohe, X_test_price_std,X_test_projects_std,X_test_qty_std)).tocsr()
print("Final Data matrix")
print(X_tr.shape, y_train.shape)
print(X_cr.shape, y_cv.shape)
print(X_te.shape, y_test.shape)
print("="*100)
2.5 Support Vector Machines with added Features `Set 5`
In [169]:
# please write all the code with proper documentation, and proper titles for each subsection
# go through documentations and blogs before you start coding
# first figure out what to do, and then think about how to do.
# reading and understanding error messages will be very much helpfull in debugging your code
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
def batch_predict(clf, data):
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
y_data_pred = []
tr_loop = data.shape[0] - data.shape[0]%1000
# consider you X_tr shape is 49041, then your cr_loop will be 49041 - 49041%1000 = 49000
# in this for loop we will iterate unti the last 1000 multiplier
for i in range(0, tr_loop, 1000):
y_data_pred.extend(clf.predict_proba(data[i:i+1000])[:,1])
# we will be predicting for the last data points
y_data_pred.extend(clf.predict_proba(data[tr_loop:])[:,1])
return y_data_pred
In [170]:
%%time
# https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import SGDClassifier
sgd=SGDClassifier()
tuned_parameters = {'alpha': [10**-4,10**-3, 10**-2,10**-1,1]}
clf = GridSearchCV(sgd, tuned_parameters, cv=3, scoring='roc_auc')
clf.fit(X_tr, y_train)
train_auc= clf.cv_results_['mean_train_score']
train_auc_std= clf.cv_results_['std_train_score']
cv_auc = clf.cv_results_['mean_test_score']
cv_auc_std= clf.cv_results_['std_test_score']
plt.plot(tuned_parameters['alpha'], train_auc, label='Train AUC')
# this code is copied from here: https://stackoverflow.com/a/48803361/4084039
plt.gca().fill_between(tuned_parameters['alpha'],train_auc - train_auc_std,train_auc + train_auc_std,alpha=0.2,color='darkblue')
plt.plot(tuned_parameters['alpha'], cv_auc, label='CV AUC')
# this code is copied from here: https://stackoverflow.com/a/48803361/4084039
plt.gca().fill_between(tuned_parameters['alpha'],cv_auc - cv_auc_std,cv_auc + cv_auc_std,alpha=0.2,color='darkorange')
plt.scatter(tuned_parameters['alpha'], train_auc, label='Train AUC points')
plt.scatter(tuned_parameters['alpha'], cv_auc, label='CV AUC points')
plt.legend()
plt.xlabel("alpha: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
# print(clf.best_estimator_)
In [171]:
%%time
# https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import SGDClassifier
sgd=SGDClassifier(penalty='l1')
tuned_parameters = {'alpha': [10**-4,10**-3,10**-2,10**-1]}
clf = GridSearchCV(sgd, tuned_parameters, cv=3, scoring='roc_auc')
clf.fit(X_tr, y_train)
train_auc= clf.cv_results_['mean_train_score']
train_auc_std= clf.cv_results_['std_train_score']
cv_auc = clf.cv_results_['mean_test_score']
cv_auc_std= clf.cv_results_['std_test_score']
plt.plot(tuned_parameters['alpha'], train_auc, label='Train AUC')
# this code is copied from here: https://stackoverflow.com/a/48803361/4084039
plt.gca().fill_between(tuned_parameters['alpha'],train_auc - train_auc_std,train_auc + train_auc_std,alpha=0.2,color='darkblue')
plt.plot(tuned_parameters['alpha'], cv_auc, label='CV AUC')
# this code is copied from here: https://stackoverflow.com/a/48803361/4084039
plt.gca().fill_between(tuned_parameters['alpha'],cv_auc - cv_auc_std,cv_auc + cv_auc_std,alpha=0.2,color='darkorange')
plt.scatter(tuned_parameters['alpha'], train_auc, label='Train AUC points')
plt.scatter(tuned_parameters['alpha'], cv_auc, label='CV AUC points')
plt.legend()
plt.xlabel("alpha: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
# print(clf.best_estimator_)
We will choose L1 regulizer
In [175]:
%%time
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve
from sklearn.metrics import roc_curve, auc
from sklearn.linear_model import LogisticRegression
from sklearn.calibration import CalibratedClassifierCV
sgd=SGDClassifier(alpha=0.0001,penalty='l1')
#https://github.com/scikit-learn/scikit-learn/issues/7278
calibrated_clf = CalibratedClassifierCV(sgd, method='sigmoid')
calibrated_clf.fit(X_tr, y_train)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
y_train_pred = batch_predict(calibrated_clf, X_tr)
y_test_pred = batch_predict(calibrated_clf, X_te)
train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)
plt.plot(train_fpr, train_tpr, label="train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("alpha: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
3. Conclusion
In [179]:
# Please compare all your models using Prettytable library
from prettytable import PrettyTable
x = PrettyTable()
x.field_names = ["Vectorizer", "Model", "Hyper Parameter","Regulizer", "AUC"]
x.add_row(["BOW", "BRUTE", 0.01,"L2", 0.83])
x.add_row(["TFIDF", "BRUTE", 0.001,"L2",0.84 ])
x.add_row(["W2V", "BRUTE", 0.0001,"L1", 0.72])
x.add_row(["TFIDFW2V", "BRUTE", 0.0001,"L1", 0.72])
x.add_row(["TruncatedSVD-Elbow method", "BRUTE", 0.0001,"L1", 0.69])
print(x)
In [ ]: